Growing community networks with local events

TL;DR

This paper introduces a dynamic model for community networks that incorporates local events, node addition, and preferential attachment, resulting in networks with power-law degree distributions.

Contribution

It presents a novel evolving community network model based on local processes and growth mechanisms, enhancing understanding of network formation.

Findings

Networks exhibit power-law degree distributions.

Model captures intra- and inter-community link dynamics.

Provides a framework for simulating real-world community networks.

Abstract

The study of community networks has attracted considerable attention recently. In this paper, we propose an evolving community network model based on local processes, the addition of new nodes intra-community and new links intra- or inter-community. Employing growth and preferential attachment mechanisms, we generate networks with a generalized power-law distribution of nodes' degrees.